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Scaling Down, LiTting Up: Efficient Zero-Shot Listwise Reranking with Seq2seq Encoder-Decoder Models

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arxiv 2312.16098 v1 pith:LAOFJSWQ submitted 2023-12-26 cs.IR

Manveer Singh Tamber , Ronak Pradeep , Jimmy Lin This is my paper · ORCID
classification cs.IR
keywords rerankingmodelslistwisezero-shotefficientmethodsparametersresults
verification ladder T0 review T1 audit T2 compute T3 formal
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Recent work in zero-shot listwise reranking using LLMs has achieved state-of-the-art results. However, these methods are not without drawbacks. The proposed methods rely on large LLMs with billions of parameters and limited context sizes. This paper introduces LiT5-Distill and LiT5-Score, two methods for efficient zero-shot listwise reranking, leveraging T5 sequence-to-sequence encoder-decoder models. Our approaches demonstrate competitive reranking effectiveness compared to recent state-of-the-art LLM rerankers with substantially smaller models. Through LiT5-Score, we also explore the use of cross-attention to calculate relevance scores to perform reranking, eliminating the reliance on external passage relevance labels for training. We present a range of models from 220M parameters to 3B parameters, all with strong reranking results, challenging the necessity of large-scale models for effective zero-shot reranking and opening avenues for more efficient listwise reranking solutions. We provide code and scripts to reproduce our results at https://github.com/castorini/LiT5.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models

    cs.CL 2025-08 conditional novelty 7.0 of 10

    On a new benchmark of post-April 2025 queries, LLM rerankers show a 5-15% performance drop compared with familiar benchmarks, and lightweight models match them on efficiency and sometimes accuracy.

  2. JointRank: Rank Large Set with Single Pass

    cs.IR 2025-06 conditional novelty 6.0 of 10

    JointRank partitions candidates into overlapping blocks, ranks each block in parallel with an LLM, and reconstructs a global ranking by aggregating the resulting pairwise comparisons.

  3. Rank-K: Test-Time Reasoning for Listwise Reranking

    cs.IR 2025-05 conditional novelty 6.0 of 10

    Rank-K, a reasoning-model-based listwise reranker distilled from DeepSeek R1 traces, beats RankZephyr on several benchmarks but only marginally on TREC DL 2019/2020.

  4. RankLLM: A Python Package for Reranking with LLMs

    cs.IR 2025-05 accept novelty 5.0 of 10

    RankLLM is an open-source Python package that modularly supports pointwise, pairwise, and listwise LLM rerankers, with integrated retrieval, evaluation, training, and response analysis, and reproduces results from Ran...

  5. Conventional Contrastive Learning Often Falls Short: Improving Dense Retrieval with Cross-Encoder Listwise Distillation and Synthetic Data

    cs.IR 2025-05 conditional novelty 5.0 of 10

    Contrastive fine-tuning often degrades strong dense retrievers, while combining cross-encoder listwise distillation with diverse synthetic queries consistently improves them.

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